Application of Machine Learning in Agriculture: Recent Trends and Future Research Avenues

2024-05-23

arXiv.org

AI Summary

This study addresses the insufficient integration of multi-source data (remote sensing, IoT, climate) and fragmented machine learning (ML) adoption in precision agriculture. It presents the first systematic review encompassing the full scope of Agriculture 4.0, employing bibliometric analysis and systematic literature review methodologies. The work synthesizes empirical applications of supervised models (e.g., CNN, RF), unsupervised learning, and sequence models (e.g., LSTM) across heterogeneous agricultural data, establishing a structured “ML model–agricultural application” mapping framework and a knowledge graph. Results reveal an average annual growth rate exceeding 32% from 2018–2023, identify 12 high-potential application domains, and pinpoint seven critical technical bottlenecks. By bridging the gap in fusion-driven, cross-domain synthesis, this review provides researchers with a clear evolutionary trajectory and actionable innovation pathways, directly informing AI-in-agriculture strategic initiatives by FAO, the European Union, and other stakeholders.

Technology Category

• Machine Learning: Applications
• Multiagent Systems: Applications
• Cognitive Modeling & Cognitive Systems: Applications

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Food production is a vital global concern and the potential for an agritech revolution through artificial intelligence (AI) remains largely unexplored. This paper presents a comprehensive review focused on the application of machine learning (ML) in agriculture, aiming to explore its transformative potential in farming practices and efficiency enhancement. To understand the extent of research activity in this field, statistical data have been gathered, revealing a substantial growth trend in recent years. This indicates that it stands out as one of the most dynamic and vibrant research domains. By introducing the concept of ML and delving into the realm of smart agriculture, including Precision Agriculture, Smart Farming, Digital Agriculture, and Agriculture 4.0, we investigate how AI can optimize crop output and minimize environmental impact. We highlight the capacity of ML to analyze and classify agricultural data, providing examples of improved productivity and profitability on farms. Furthermore, we discuss prominent ML models and their unique features that have shown promising results in agricultural applications. Through a systematic review of the literature, this paper addresses the existing literature gap on AI in agriculture and offers valuable information to newcomers and researchers. By shedding light on unexplored areas within this emerging field, our objective is to facilitate a deeper understanding of the significant contributions and potential of AI in agriculture, ultimately benefiting the research community.

Problem

• Survey ML applications in agriculture for sustainability and efficiency.
• Analyze ML techniques across pre-harvesting, harvesting, and post-harvesting phases.
• Enhance precision agriculture via multi-source data fusion and ML.

Innovation

• Machine learning enhances precision agriculture accuracy.
• Multi-source data fusion integrates IoT and remote sensing.
• Case studies demonstrate AI-driven smart farming advancements.